CN110213715B - V2V communication mode switching method based on optimal estimation distance between vehicles - Google Patents
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Abstract
The invention discloses a V2V communication mode switching method based on optimal estimated distance between vehicles, which comprises the following steps: predicting a state estimation value of the vehicle at the current moment according to the relevant variable of the vehicle state; updating the state estimation value of the vehicle according to the predicted state estimation value of the vehicle at the current moment and the measurement data of the vehicle state to obtain an optimal state estimation value; obtaining the optimal estimated distance between vehicles according to the optimal state estimation value; and acquiring the probability of carrying out V2V communication mode switching decision according to a probability density function of the square of the estimated distance between the vehicles, the optimal estimated distance between the vehicles and a set distance threshold value of direct communication between the vehicles, and switching the V2V communication mode according to the probability. Compared with a V2V communication mode switching method based on a measured distance, the V2V communication mode switching method based on the optimal estimated distance between the vehicles selects a proper communication mode for the V2V vehicle pair, and improves the average throughput of the V2V communication link.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a V2V communication mode switching method based on optimal estimated distance between vehicles.
Background
In an Intelligent Transport System (ITS), in order to improve safety of vehicle driving, it is necessary to implement mutual communication between a pair of vehicles so as to transmit information such as an early warning message and a vehicle position. During the dynamic driving process of the vehicle, the establishment of a proper V2V communication mode in the vehicle pair can effectively improve the V2V communication link performance. In traditional vehicle networking communication, the vehicle learns the position information of surrounding vehicles by analyzing and processing data collected by sensors such as laser radar and the like, and the vehicle can successfully establish connection only when the distance is within a signal range, so that the time delay is large and the reliability is low. In addition, when the number of vehicles on the road is increased, the data amount of the sensors is increased, the real-time performance and accuracy of sensing the positions of other vehicles around the vehicle are reduced, and the switching of the V2V communication mode by the vehicle is not facilitated. Therefore, consider a V2V communication mode switch in a cellular internet of vehicles scenario. In the cellular internet of vehicles, a base station is a control center of the whole system, and can receive measurement data, namely driving state data, of all vehicles in the coverage area of the base station in real time. At different times, the base station selects a proper V2V communication mode according to the vehicle-to-distance, and the dynamic V2V communication mode is adaptively switched.
In the existing V2V communication mode switching method, V2V communication mode switching by a measured distance of a vehicle pair is a commonly used mode switching method. But the measurement data of the vehicle state is noisy, causing uncertainty in the vehicle position. Therefore, during dynamic driving of the vehicle, the mode switching method based on the inter-vehicle measurement distance may select an inappropriate V2V communication mode for the V2V vehicle pair, resulting in a lower average throughput of the V2V communication link in the cellular internet of vehicles.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide a V2V communication mode switching method based on the optimal estimated distance between vehicles, and aims to solve the problem that the average throughput of a V2V communication link is low due to the fact that V2V communication mode switching is carried out according to the measured distance.
In order to achieve the above object, the present invention provides a V2V communication mode switching method based on an optimal estimated distance between vehicles, comprising:
(1) acquiring the optimal estimation distance between vehicles at the current moment by using Kalman filtering according to the measurement data of the vehicle state and the relevant variables of the vehicle state;
(2) and acquiring the probability that the V2V communication mode is the direct-through mode according to the optimal estimated distance between the vehicles, and switching the V2V communication mode according to the probability.
Preferably, the relevant variables of the vehicle state include: a state transition matrix, a control matrix, a measurement noise covariance matrix, a process noise covariance matrix, a state estimate, an error covariance matrix, and an observation matrix.
Preferably, step (1) comprises:
(1.1) predicting a state estimation value of the vehicle at the current moment and a state error covariance matrix at the current moment according to the relevant variables of the vehicle state;
and (1.2) updating the Kalman increment, the state estimation value and the error covariance matrix according to the predicted state estimation value, the error covariance matrix, the observation matrix, the measurement noise covariance matrix of the vehicle at the current moment and the measurement data of the vehicle state to obtain the optimal estimation distance between the vehicles at the current moment.
Preferably, step (1.1) is:
predicting the state estimation value of the vehicle at the current moment according to the state estimation value, the state transition matrix, the control matrix and the process noise of the vehicle at the previous moment;
and predicting the error covariance matrix of the vehicle state at the current moment according to the error covariance matrix of the vehicle state at the previous moment, the state transition matrix and the process noise covariance matrix.
The step (1.2) comprises the following steps:
(1.2.1) updating Kalman increment according to the predicted error covariance matrix, the observation matrix and the measurement noise covariance matrix of the vehicle state at the current moment;
(1.2.2) updating the error covariance matrix of the vehicle state at the current moment according to the Kalman increment, the predicted error covariance matrix of the vehicle state at the current moment and the observation matrix;
and updating the state estimation value of the vehicle according to the measurement data of the vehicle state, the predicted state estimation value of the vehicle at the current moment, the observation matrix and the Kalman increment, and acquiring the optimal estimation position of the vehicle and the optimal estimation distance between the vehicles.
Preferably, step (2) comprises:
obtaining a probability density function of the square of the estimated distance of the vehicle pair according to the updated state estimated value of the vehicle at the current moment and the Gaussian distribution and the optimal estimated distance of the vehicle pair at the current moment;
and acquiring the probability of switching the V2V communication mode according to the probability density function of the square of the estimated distance of the vehicle pair, the optimal estimated distance of the vehicle pair at the current moment and the set distance threshold value of the direct communication between the vehicles, and switching the V2V communication mode according to the probability.
Preferably, the V2V communication mode is a pass-through mode or a cellular mode.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
according to the invention, vehicle measurement data are filtered through a Kalman filtering algorithm, and the obtained optimal estimated position of the vehicle is more accurate than the measured position. Compared with a V2V communication mode switching method based on a measured distance, the V2V communication mode switching method based on the optimal estimated distance between vehicles can select a proper communication mode for the V2V vehicle pair, the average throughput of a V2V communication link is improved, and the self-adaptive switching of the dynamic V2V communication mode is realized.
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FIG. 1 is a diagram of a communication scenario of V2V in the cellular Internet of vehicles provided by the present invention;
FIG. 2 is a flow chart of a V2V communication mode switching method based on an optimal estimated distance between vehicles according to the present invention;
FIG. 3 is a comparison graph of the average throughput simulation of the V2V communication link based on the optimal estimated distance between vehicles and the mode switching method based on the measured distance.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a V2V communication mode switching method based on optimal estimated distance between vehicles, which can improve the average throughput of a V2V communication link by carrying out dynamic V2V communication mode self-adaptive switching through a base station.
Fig. 1 is a diagram of a V2V communication scenario for cellular internet of vehicles, including two V2V communication modes and a handoff scenario corresponding to the two V2V communication modes. As shown in fig. 1, the communication coverage of the cellular network is centered around the base station and has a radius RcThe cellular network covers a part of the road, the length of the covered road section is L, the distance from the center of the road to the base station is D, and D is satisfied2+(L/2)2＝Rc 2(ii) a A plurality of cellular users are distributed randomly and uniformly in the coverage area of the cellular network, N V2V vehicle pairs are arranged on a road section covered by the cellular network, the vehicles are distributed randomly and uniformly, and the vehicle pairs are collected into
Vehicle pair ViBy transmitting end vehicle Vi TAnd the receiving end vehicle Vi RThe V2V communication mode comprises a direct mode and a cellular mode, and when the distance between vehicles is too large at different moments, the V2V communication path loss in the direct mode is too much, and the inter-vehicle communication mode is switched to the cellular mode; switching to the pass-through mode can improve the performance of the communication link when the inter-vehicle distance is close.
As shown in fig. 2, the present invention provides a V2V communication mode switching method based on an optimal estimated distance between vehicles, comprising:
(1) acquiring the optimal estimation distance between vehicles at the current moment by using Kalman filtering according to the measurement data of the vehicle state and the relevant variables of the vehicle state;
the relevant variables of the vehicle state include: a state transition matrix, a control matrix, a measurement noise covariance matrix, a process noise covariance matrix, a state estimate, an error covariance matrix, and an observation matrix.
More specifically, according to the uniform speed change linear motion formula, a vehicle state transition matrix F and a control matrix B can be obtained, and the expressions are respectively:
where Δ t represents the time interval over which measurement data is acquired.
The measurement noise covariance matrix R and the process noise covariance matrix Q are obtained according to actually measured state data;
initial time vehicle state estimation value
And the error covariance matrix P of the vehicle state at the initial time0Obtaining according to actual conditions;
the observation matrix H is an identity matrix because the measured data of the vehicle and the vehicle state variables are consistent.
The step (1) specifically comprises:
(1.1) predicting a state estimation value of the vehicle at the current moment and an error covariance matrix of the vehicle state at the current moment according to the relevant variables of the vehicle state;
step (1.1) more specifically comprises:
predicting the state estimation value of the vehicle at the current moment according to the state estimation value, the state transition matrix, the control matrix and the process noise of the vehicle at the previous moment;
and predicting the error covariance matrix of the vehicle state at the current moment according to the error covariance matrix of the vehicle state at the previous moment, the state transition matrix and the process noise covariance matrix.
I.e. the predicted estimated value of the state of the vehicle at the present time
Comprises the following steps:
wherein,
is a predicted state estimation value of the vehicle at the current time;
is an estimate of the state of the vehicle at a previous time, ut＝[ux,tuy,t]TRepresenting the vehicle acceleration at the present time; w is atIs process noise, and wtN (0, Q); q is the process noise covariance matrix.
Error covariance matrix of predicted current time vehicle state
Comprises the following steps:
wherein,
an error covariance matrix for the predicted current time vehicle state; f is a vehicle state transition matrix; pt-1An error covariance matrix of a vehicle state at a previous time;
and (1.2) updating the Kalman increment, the state estimation value and the error covariance matrix according to the predicted state estimation value, the error covariance matrix, the observation matrix, the measurement noise covariance matrix of the vehicle at the current moment and the measurement data of the vehicle state to obtain the optimal estimation distance between the vehicles at the current moment. The step (1.2) specifically comprises the following steps:
(1.2.1) updating Kalman increment according to the predicted error covariance matrix, the observation matrix and the measurement noise covariance matrix of the vehicle state at the current moment;
the kalman increment is:
Ktis the Kalman increment of the current moment;
an error covariance matrix for the predicted current time vehicle state; h is an observation matrix; r is the measurement noise covarianceA matrix;
(1.2.2) updating the error covariance matrix of the vehicle state at the current moment according to the Kalman increment, the predicted error covariance matrix of the vehicle state at the current moment and the observation matrix;
and updating the state estimation value of the vehicle at the current moment according to the measurement data of the vehicle state, the predicted state estimation value of the vehicle at the current moment, the observation matrix and the Kalman increment to obtain the optimal estimation distance between the vehicles.
The optimal state estimation value of the vehicle is as follows:
wherein r istMeasurement data representing a vehicle state, whose expression:
rt＝Hxt+vt
wherein v istIs measurement noise, and vt～N(0,R)；xtIs a vehicle state estimate; ktIs the Kalman increment of the current moment;
is a predicted state estimation value of the vehicle at the current time; h is an observation matrix;
estimating value according to optimal state of vehicle
Can determine the vehicle pair ViTransmitting end vehicle V ini TAnd a receiving end vehicle Vi RPosition of the vehicle V at the transmitting endi TAnd a receiving end vehicle Vi RThe position coordinates after Kalman filtering are respectively
And
and is
Respectively represent vehicles Vi TThe optimal estimated position in the x-axis and y-axis,
representing the variance of the noise;
respectively represent vehicles Vi RThe optimal estimated position in the x-axis and y-axis,
representing the variance of the noise. Then the vehicle Vi TAnd a vehicle Vi RThe estimated distance between is:
vehicle Vi TAnd a vehicle Vi RThe optimal estimated distance between the two is:
the error covariance matrix of the vehicle state at the current time is:
wherein,
a state error covariance matrix for the predicted current time; ktIs the Kalman increment of the current moment; h is an observation matrix;
(2) acquiring the probability that the V2V communication mode is the direct mode according to the optimal estimated distance of the vehicle pair and the state estimated value of the vehicle at the current moment, and switching the V2V communication mode according to the probability;
the step (2) specifically comprises the following steps:
(2.1) obtaining a probability density function of the square of the estimated distance of the vehicle pair according to the updated state estimated value of the vehicle at the current moment and the Gaussian distribution and the optimal estimated distance of the vehicle pair at the current moment;
specifically, let
Then there is
Order to
Then there is
And d isi 2＝x'2+y'2。di 2Is composed of the sum of squares of two independent, homovariance, different mean gaussian variables. Thus, di 2Non-central chi-square distribution subject to a degree of freedom of 2, di 2The probability density function of (a) is:
wherein,
indicating vehicle Vi TAnd a vehicle Vi RThe optimal estimated distance therebetween. I is0(. cndot.) is a first class of modified Bessel function, which is a Gamma function, I0The general expression of (·) is:
(2.2) acquiring the probability of switching the V2V communication mode according to the probability density function of the square of the estimated distance of the vehicle pair, the optimal estimated distance of the vehicle pair at the current moment and the set distance threshold value of the direct communication between the vehicles, and switching the V2V communication mode according to the probability.
In particular, due to the vehicle pair ViCan be based on the transmitting end vehicle Vi TAnd a receiving end vehicle Vi REstimated distance d betweeniTo select, and thus can utilize
For the selection of the V2V communication mode, different from diCorresponding to different optimal estimated distances between vehicles
But the variancei 2Related to environmental factors, can be set to constant values2. Assume that the distance threshold satisfying direct inter-vehicle communication is dthThen at time t, vehicle Vi TAnd a vehicle Vi RThe probability of switching the inter-communication mode to the pass-through mode is:
according to the technical scheme provided by the invention, the vehicle measurement data are filtered through a Kalman filtering algorithm, and the obtained optimal estimated position of the vehicle is more accurate than the measured position. Compared with the V2V communication mode switching method based on the measured distance, the V2V communication mode switching method based on the optimal estimated distance between the vehicles can select a proper communication mode for the V2V vehicle pair, and the average throughput of the V2V communication link is improved.
To better embody the concept of throughput, the following briefly introduces the vehicle pair ViCalculation of the average throughput of the communication link.
Vehicle pair ViThe through link throughput of (d) is:
vehicle pair ViThe cellular link throughput of (c) is:
under the switching of the V2V communication mode, the vehicle is opposite to ViThe average throughput of the communication link is:
wherein, PVFor vehicles Vi TThe transmit power of (a); h isi,iFor vehicles Vi TAnd Vi RInter-communication link channel gain; h isi,BSFor vehicles Vi TChannel gain of a communication link with a base station; n is a radical of0Is the noise power spectral density; b is the bandwidth size of the spectrum resource block allocated by the base station.
FIG. 3 is a comparison graph of the average throughput simulation of the V2V communication link based on the optimal estimated distance between vehicles and the mode switching method based on the measured distance; the relevant parameters employed in fig. 3 are as follows:
Rc＝500m，
D＝250m，PV＝23dBm，N＝40，N0＝-174dBm/Hz，B＝0.25MHz，Δt＝1s，dth＝80m，2＝12，R＝diag([3 3 1 0])，Q＝diag([1 1 0.1 0])，P0＝diag([1010 1 0])，
equal to the initial actual measured state plus covariance as P0White gaussian noise of (1);
fig. 3 shows average throughput of V2V communication links based on different mode switching methods at the same time, and since the transmitting end vehicles and the receiving end vehicles of all the vehicle pairs travel in opposite directions at the initial time, the number of vehicle pairs initially adopting the pass-through mode increases, and the path loss of signals is smaller, so that the average throughput increases. After 8s, the mode switching method based on the optimal estimated distance between the vehicles is superior to the mode switching method based on the measured distance between the vehicles in terms of the average throughput of the V2V communication link. This is because the optimal estimated position obtained by the vehicle state prediction model is closer to the true position of the vehicle than the measured position.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (2)
1. A V2V communication mode switching method based on optimal estimated distance between vehicles is characterized by comprising the following steps:
(1) acquiring the optimal estimation distance between vehicles at the current moment by using Kalman filtering according to the measurement data of the vehicle state and the relevant variables of the vehicle state;
(2) according to the optimal estimated distance of the vehicle pair, the probability that the V2V communication mode is the direct mode is obtained, and the V2V communication mode is switched according to the probability;
the step (1) comprises the following steps:
(1.1) predicting a state estimation value of the vehicle at the current moment and an error covariance matrix of the vehicle state at the current moment according to the relevant variables of the vehicle state;
(1.2) updating the Kalman increment, the state estimation value and the error covariance matrix according to the predicted state estimation value, the error covariance matrix, the observation matrix, the measurement noise covariance matrix of the vehicle at the current moment and the measurement data of the vehicle state to obtain the optimal estimation distance between the vehicles at the current moment;
the step (1.1) is as follows:
predicting the state estimation value of the vehicle at the current moment according to the state estimation value, the state transition matrix, the control matrix and the process noise of the vehicle at the previous moment;
predicting an error covariance matrix of the vehicle state at the current moment according to the error covariance matrix of the vehicle state at the previous moment, the state transition matrix and the process noise covariance matrix;
the step (1.2) comprises:
(1.2.1) updating Kalman increment according to the predicted error covariance matrix, the observation matrix and the measurement noise covariance matrix of the vehicle state at the current moment;
(1.2.2) updating the error covariance matrix of the vehicle state at the current moment according to the Kalman increment, the predicted error covariance matrix of the vehicle state at the current moment and the observation matrix;
updating the state estimation value of the vehicle at the current moment according to the measurement data of the vehicle state, the predicted state estimation value of the vehicle at the current moment, the observation matrix and the Kalman increment to obtain the optimal estimation distance between the vehicles;
the optimal estimated distance between vehicles is:
wherein,
respectively represent vehicles Vi TThe optimal estimated position in the x-axis and y-axis,
respectively represent vehicles Vi RAn optimal estimated position in the x-axis and the y-axis; vehicle Vi TAnd a vehicle Vi RIs determined from the updated state estimation value of the vehicle at the current timeDetermining; the updated state estimation value of the vehicle at the current moment is obtained by performing Kalman filtering processing on the predicted state estimation value of the vehicle at the current moment;
the step (2) comprises the following steps:
obtaining a probability density function of the square of the estimated distance of the vehicle pair according to the updated estimated position in the state estimated value of the vehicle at the current moment and the Gaussian distribution and the optimal estimated distance of the vehicle pair at the current moment;
and acquiring the probability of switching the V2V communication mode according to the probability density function of the square of the estimated distance of the vehicle pair, the optimal estimated distance of the vehicle pair at the current moment and the set distance threshold value of the direct communication between the vehicles, and switching the V2V communication mode according to the probability.
2. The V2V communication mode switching method of claim 1, wherein the V2V communication mode is a pass-through mode or a cellular mode.
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